{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is to test TTS models with benchmark sentences for speech synthesis.\n",
    "\n",
    "Before running this script please DON'T FORGET: \n",
    "- to set file paths.\n",
    "- to download related model files from TTS and PWGAN.\n",
    "- download or clone related repos, linked below.\n",
    "- setup the repositories. ```python setup.py install```\n",
    "- to checkout right commit versions (given next to the model) of TTS and PWGAN.\n",
    "- to set the right paths in the cell below.\n",
    "\n",
    "Repositories:\n",
    "- TTS: https://github.com/mozilla/TTS\n",
    "- PWGAN: https://github.com/erogol/ParallelWaveGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "import sys\n",
    "import io\n",
    "import torch \n",
    "import time\n",
    "import json\n",
    "import yaml\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (16,5)\n",
    "\n",
    "import librosa\n",
    "import librosa.display\n",
    "\n",
    "from TTS.models.tacotron import Tacotron \n",
    "from TTS.layers import *\n",
    "from TTS.utils.data import *\n",
    "from TTS.utils.audio import AudioProcessor\n",
    "from TTS.utils.generic_utils import load_config, setup_model\n",
    "from TTS.utils.text import text_to_sequence\n",
    "from TTS.utils.synthesis import synthesis\n",
    "from TTS.utils.visual import visualize\n",
    "\n",
    "import IPython\n",
    "from IPython.display import Audio\n",
    "\n",
    "import os\n",
    "\n",
    "# you may need to change this depending on your system\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
    "    t_1 = time.time()\n",
    "    waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, False, CONFIG.enable_eos_bos_chars)\n",
    "    if CONFIG.model == \"Tacotron\" and not use_gl:\n",
    "        # coorect the normalization differences b/w TTS and the Vocoder.\n",
    "        mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
    "    mel_postnet_spec = ap._denormalize(mel_postnet_spec)\n",
    "#     mel_postnet_spec = np.pad(mel_postnet_spec, pad_width=((2, 2), (0, 0)))\n",
    "    print(mel_postnet_spec.shape)\n",
    "    print(\"max- \", mel_postnet_spec.max(), \" -- min- \", mel_postnet_spec.min())\n",
    "    if not use_gl:\n",
    "        waveform = vocoder_model.inference(torch.FloatTensor(ap_vocoder._normalize(mel_postnet_spec).T).unsqueeze(0), hop_size=ap_vocoder.hop_length)\n",
    "#         waveform = waveform / abs(waveform).max() * 0.9\n",
    "    if use_cuda:\n",
    "        waveform = waveform.cpu()\n",
    "    waveform = waveform.numpy()\n",
    "    rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
    "    print(waveform.shape)\n",
    "    print(\" > Run-time: {}\".format(time.time() - t_1))\n",
    "    print(\" > Real-time factor: {}\".format(rtf))\n",
    "    if figures:  \n",
    "        visualize(alignment, mel_postnet_spec, stop_tokens, text, ap.hop_length, CONFIG, ap._denormalize(mel_spec))                                                                       \n",
    "    IPython.display.display(Audio(waveform, rate=CONFIG.audio['sample_rate'], normalize=False))  \n",
    "    os.makedirs(OUT_FOLDER, exist_ok=True)\n",
    "    file_name = text.replace(\" \", \"_\").replace(\".\",\"\") + \".wav\"\n",
    "    out_path = os.path.join(OUT_FOLDER, file_name)\n",
    "    ap.save_wav(waveform, out_path)\n",
    "    return alignment, mel_postnet_spec, stop_tokens, waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set constants\n",
    "ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-bn-December-23-2019_08+34AM-ffea133/'\n",
    "MODEL_PATH = ROOT_PATH + '/checkpoint_670000.pth.tar'\n",
    "CONFIG_PATH = ROOT_PATH + '/config.json'\n",
    "OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
    "CONFIG = load_config(CONFIG_PATH)\n",
    "VOCODER_MODEL_PATH = \"/home/erogol/Models/LJSpeech/pwgan-ljspeech/checkpoint-400000steps.pkl\"\n",
    "VOCODER_CONFIG_PATH = \"/home/erogol/Models/LJSpeech/pwgan-ljspeech/config.yml\"\n",
    "\n",
    "# load PWGAN config\n",
    "with open(VOCODER_CONFIG_PATH) as f:\n",
    "    VOCODER_CONFIG = yaml.load(f, Loader=yaml.Loader)\n",
    "    \n",
    "# Run FLAGs\n",
    "use_cuda = False\n",
    "# Set some config fields manually for testing\n",
    "CONFIG.windowing = True\n",
    "CONFIG.use_forward_attn = True \n",
    "# Set the vocoder\n",
    "use_gl = False # use GL if True\n",
    "batched_wavernn = True    # use batched wavernn inference if True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD TTS MODEL\n",
    "from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
    "\n",
    "# multi speaker \n",
    "if CONFIG.use_speaker_embedding:\n",
    "    speakers = json.load(open(f\"{ROOT_PATH}/speakers.json\", 'r'))\n",
    "    speakers_idx_to_id = {v: k for k, v in speakers.items()}\n",
    "else:\n",
    "    speakers = []\n",
    "    speaker_id = None\n",
    "\n",
    "# if the vocabulary was passed, replace the default\n",
    "if 'characters' in CONFIG.keys():\n",
    "    symbols, phonemes = make_symbols(**CONFIG.characters)\n",
    "\n",
    "# load the model\n",
    "num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
    "model = setup_model(num_chars, len(speakers), CONFIG)\n",
    "\n",
    "# load the audio processor\n",
    "ap = AudioProcessor(**CONFIG.audio)         \n",
    "\n",
    "\n",
    "# load model state\n",
    "cp =  torch.load(MODEL_PATH, map_location=torch.device('cpu'))\n",
    "\n",
    "# load the model\n",
    "model.load_state_dict(cp['model'])\n",
    "if use_cuda:\n",
    "    model.cuda()\n",
    "model.eval()\n",
    "print(cp['step'])\n",
    "print(cp['r'])\n",
    "\n",
    "# set model stepsize\n",
    "if 'r' in cp:\n",
    "    model.decoder.set_r(cp['r'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD WAVERNN\n",
    "if use_gl == False:\n",
    "    from parallel_wavegan.models import ParallelWaveGANGenerator\n",
    "    from parallel_wavegan.utils.audio import AudioProcessor as AudioProcessorVocoder\n",
    "    \n",
    "    vocoder_model = ParallelWaveGANGenerator(**VOCODER_CONFIG[\"generator_params\"])\n",
    "    vocoder_model.load_state_dict(torch.load(VOCODER_MODEL_PATH, map_location=\"cpu\")[\"model\"][\"generator\"])\n",
    "    vocoder_model.remove_weight_norm()\n",
    "    ap_vocoder = AudioProcessorVocoder(**VOCODER_CONFIG['audio'])    \n",
    "    if use_cuda:\n",
    "        vocoder_model.cuda()\n",
    "    vocoder_model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparision with https://mycroft.ai/blog/available-voices/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "model.decoder.max_decoder_steps = 2000\n",
    "model.decoder.prenet.eval()\n",
    "speaker_id = None\n",
    "sentence = '''A breeding jennet, lusty, young, and proud,'''\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence =  \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### https://espnet.github.io/icassp2020-tts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The Commission also recommends\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"As a result of these studies, the planning document submitted by the Secretary of the Treasury to the Bureau of the Budget on August thirty-one.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The FBI now transmits information on all defectors, a category which would, of course, have included Oswald.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"they seem unduly restrictive in continuing to require some manifestation of animus against a Government official.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"and each agency given clear understanding of the assistance which the Secret Service expects.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Be a voice, not an echo.\"  # 'echo' is not in training set. \n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The human voice is the most perfect instrument of all.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"This cake is great. It's so delicious and moist.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison with https://keithito.github.io/audio-samples/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison with https://google.github.io/tacotron/publications/tacotron/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Basilar membrane and otolaryngology are not auto-correlations.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \" He has read the whole thing.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"He reads books.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Thisss isrealy awhsome.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"This is your internet browser, Firefox.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"This is your internet browser Firefox.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The quick brown fox jumps over the lazy dog.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Does the quick brown fox jump over the lazy dog?\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Eren, how are you?\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hard Sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Encouraged, he started with a minute a day.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"His meditation consisted of “body scanning” which involved focusing his mind and energy on each section of the body from head to toe .\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning . \"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"If he decided to watch TV he really watched it.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"Often we try to bring about change through sheer effort and we put all of our energy into a new initiative .\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for twb dataset\n",
    "sentence = \"In our preparation for Easter, God in his providence offers us each year the season of Lent as a sacramental sign of our conversion.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
   ]
  }
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